Background The purpose of the present study is to evaluate the prevalence of temporomandibular disorders (TMD) and their associated biological and psychological factors in Chinese university students. Methods A total of 754 students were included from Zunyi Medical University, each participant completed questionnaires and clinical examinations according to the Diagnostic Criteria for Temporomandibular Disorders. Results The overall prevalence of TMD was 31.7% among medical students. Subjects with TMD had a high prevalence of bruxism, empty chewing, unilateral chewing, chewing gum, anterior teeth overbite, anterior teeth overjet, depression, anxiety, and sleep disturbance. Moreover, sleep bruxism, empty chewing, unilateral chewing, anterior teeth overbite, depression, and anxiety were the strongest risk factors for TMD. Conclusions Individuals with TMD have a high prevalence of psychological distress and oral parafunctional habits. Except for the psychological factors associated with TMD, bruxism, abnormal chewing, and malocclusion also shared similar risks for TMD.
Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant’s stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.